Sparky

While crystallography and NMR are useful for defining the structural characteristics of proteins, cryo-electron microscopy (cryo-EM) may be the most useful technique for investigating the structure of large biomolecular assemblies. Rapid advances in the technique have brought it to the point where it can deliver atomic-resolution models, without the need for crystallization or any relevant upper limit on the size of the particle to be studied.

Under certain circumstances, however, it can be difficult for cryo-EM to determine interior details of these complexes. For instance, the arrangement of protein and DNA inside the bacteriophage ΦKZ, a potentially therapeutic virus that attacks Pseudomonas aeruginosa, cannot easily be visualized because these components are coiled together so closely. In a paper published in Science last week (1), a team from the NIH and the University of Maryland addressed this problem by destroying the protein component and using cryo-EM to characterize the void left behind.

Hitting ice-embedded virus particles with large amounts of electrons caused the formation of “bubbles” that could be seen in the electron micrographs. This bombardment created high-pressure bubbles of hydrogen that destroyed the internal protein at relatively low radiation levels. The external proteins composing the viral capsid, however, survived the treatment. The authors propose that the surrounding nucleic acid prevented the radiation products from diffusing away, so that the interior proteins became more sensitive to radiation damage.

Using cryo-EM studies of the irradiated capsids, the authors were able to determine the location and shape of the inner protein mass by examining the void left behind when it was destroyed. They found that it had a multi-tiered structure with six-fold symmetry, and that it was positioned at an angle that matched the DNA packing. This suggests that the inner body assists in organizing and packaging the DNA.

In addition to providing insight about this particular virus, the authors suggest that this approach could be used to study other challenging subjects. They specifically mention condensed chromatin as a potential target, but in principle this method could be applied to any situation where materials with differential sensitivity to radiation are tightly packed together.

One of the most serious challenges facing medical science today is the development of drug resistance by bacteria and viruses. Almost as quickly as we can develop drugs that attack the machinery of infectious disease, evolution, aided in some cases by careless use, defeats our efforts. In some cases this is because the specific target of a drug changes in response to the challenge, as has happened in the evolution of resistance to rimantidine in influenza. Bacteria have an additional mechanism to attack our medicines, however, in the form of multidrug resistance genes. These proteins can recognize an array of toxic molecules, often using general properties, and expel them from the cell. As such, every single one of these genes can take out multiple medicines.

One of these multidrug resistance exporters is EmrE, a member of the small multidrug resistance (SMR) family of genes. EmrE is a proton-drug antiporter that pushes positively-charged polyaromatic molecules out of the cell while letting two protons in. The import of the protons provides the energy to expel the toxins against a concentration gradient. Today in Nature, a research group led by my friend Katie Henzler-Wildman published new details of EmrE’s mechanism and topology (1). Using NMR and fluorescence techniques, we show that EmrE does, or at least can, operate as an antiparallel, asymmetric dimer that exposes a single active site to alternating sides of the membrane by simultaneously switching the conformation of the monomers.

This was a long and difficult project, in which I played a small role. Unfortunately, multidrug resistance exporters like EmrE tend to be integrated into the bacterial membrane, which makes them challenging subjects for biophysical studies. In order to investigate proteins like this, we must reconstitute them in lipid environments that suitably mimic their natural setting, while maintaining sufficient purity and concentration to perform our experiments. The controversy over the effect of rimantidine on influenza’s M2 channel provides just one example of the difficulty of reliably recreating a membrane environment.

EmrE has also been embroiled in a controversy between structural biologists and biochemists. Although the minimal functional unit is agreed to be a dimer, biochemical studies have indicated that that the dimer is symmetric, and that the proteins have a parallel orientation in the membrane. That is, each EmrE protein has the same shape and is pointing the same way. Relatively low-resolution data from crystallography and electron microscopy, however, has suggested that the protein units are asymmetric and antiparallel. However, these studies were performed in lipid environments where the protein may not have been active, and at frozen temperatures far from physiological relevance. One would like to get a look at EmrE in a state where it is active and at a somewhat more reasonable temperature.

Caught in the Act

Solution NMR provides one way to achieve this goal. A protein can be embedded in a small bit of membrane, and allowed to tumble freely in an aqueous environment, allowing sufficient signal for us to make some kinds of measurements. Historically, micelles have been used for this, but multiple lines of evidence now suggest that they may produce artifacts due to the unnatural local curvature. Consequently, Katie and her student Emma worked out a system for observing EmrE in bicelles, which are small, flat-ish discs of lipids that still tumble freely enough to allow solution-state NMR measurements. They also established that EmrE in this bicelle preparation could still bind to tetraphenylphosphonium (TPP+), one of its ligands.

The NMR spectrum, however, was perplexing. As you can see in the HSQC to the right, the peaks in the spectrum are fairly spread out. That’s unusual for a protein composed entirely of α-helices, but because electron currents from the aromatic rings of TPP+ induce significant changes in chemical shift it’s still reasonable. What is more troubling, and perhaps less obvious, is that there are twice as many peaks in this spectrum as you would expect.

An HSQC of a 15N-labeled protein, in principle, shows one peak for every N-H in the protein, such that you get a two-dimensional spectrum showing the chemical shift of the nitrogen on one axis and its bonded proton on the other. This means there should be one peak per amino acid, except prolines. In addition, peaks usually appear for tryptophan indoles (they are at bottom left in the spectrum) and, depending on your setup, glutamine and asparagine side-chains. Other side-chains usually exchange with water too quickly to be seen. EmrE has about 100 residues, and the spectrum has about 200 peaks. This indicates that there are two different structures of EmrE in the sample.

We decided to ask whether EmrE switched between these two structures and how fast. Observing two peaks per residue, of roughly equal intensity, told us that if EmrE did change its structure, it was doing so slowly, at a rate of 10 times a second or less. So we used an experiment called ZZ-exchange, which is similar to the HSQC but includes a relatively long pause between determining the 15N chemical shift and the 1H chemical shift. If a significant proportion of the sample changes conformation during the pause, you will see a spectrum that includes all the HSQC peaks, as well as cross-peaks that have the 15N chemical shift of one structure and the 1H chemical shift of the other, producing a little rectangle of peaks. This ended up being tricky because the bicelle distorts the signals, but Katie, Greg DeKoster, and I managed to come up with a setup that got around this problem on the 800 at Brandeis, starting from a pulse sequence written by Art Palmer.

As you can see above, we observed cross peaks, shown in blue to differentiate them from the HSQC peaks. By varying the pause between chemical shift determinations, we were able to fit a rate of about 5 /s, which roughly correlates to a fluorescence fluctuation observed in previous experiments. In addition, experiments using paramagnetic relaxation enhancement agents show that the two states have different accessibility to water, suggesting that they represent a change in which side of the active site is open. This supports the conclusion that what we are seeing here is the fundamental conformational change inherent to EmrE’s function: the opening of the binding site to one side of the membrane, then the other.

The ABBA Model?

So now we have evidence for two distinct structures that interconvert during the export process. Two models can be consistent with these data, shown in the figure below. The most obvious possibility, consistent with almost all of the biochemical data, is that the structures represent two states of a symmetric, parallel dimer converting from an AA state to a BB state. Alternately, consistent with the crystallographic data, one could have an asymmetric, antiparallel dimer that exchanges from an AB state to a BA state.

The NMR data support the second model in two ways. The first is that peaks for the two states have almost equal intensity, which can only be the case if both states are almost equal in free energy. This happens automatically in the case of the asymmetric dimer, because each dimer contains one of each conformation, making the exchange to the alternate state energy-neutral. In the case of a symmetric dimer, it requires that each individual conformation have the same energy, which is unlikely, but not impossible. Also, in the NMR data, regions of the protein that show the largest difference in chemical shift between the two states also show the most significant conformational differences in the crystal structure of the asymmetric dimer. Unfortunately, these lines of evidence are not enough to be sure about what we’re seeing.

Flash in the Pan

To get a better idea of EmrE’s topology, Katie and her team performed a number of FRET and crosslinking experiments to establish the relative orientation of dimers in the membrane. In bulk FRET experiments, they fluorescently labeled EmrE that was in liposomes, as shown in Figure 3. In the first experiment, EmrE was exposed to one label while in liposomes, then broken out into bicelles and exposed to another. For antiparallel proteins, excitation of the green dye should result in fluorescent output from the red dye, and this is exactly what was observed. Also, EmrE in liposomes was exposed to both dyes simultaneously, which should result in an observation of FRET for parallel but not antiparallel dimers. Some FRET was observed, but it wasn’t clear whether this was due to dye leaking into the liposomes.

Katie answered this question using single-molecule FRET. EmrE dimers with a single cysteine mutated into them were labeled with fluorescent dye and then examined on a slide to determine the efficiency of energy transfer. Because there is only one labeling site, a high-efficiency transfer would imply that both fluorophores were on the same side of the membrane, and thus a parallel topology. However, the observed efficiency suggested a distance of 50 Å between the fluorophores, more consistent with an antiparallel topology where the labeling sites are separated by the membrane.

In one final experiment, Katie used a molecule that covalently links a lysine side chain to a cysteine side chain. There is only one lysine in EmrE, and Katie created a mutant that has a single cysteine on the opposite side of the membrane. This distance is too great for the linker molecule to bridge, so in a parallel dimer no cross-linking should be observed. Instead, the experiment resulted in nearly complete cross-linking, supporting an antiparallel topology.

How an Antiporter Works

Cumulatively, these results strongly support the model shown below, where EmrE swaps two protons for a drug molecule using a conformational exchange between energetically-equivalent asymmetric, antiparallel dimer states that are open to different sides of the membrane. In this model, there is a single binding site, consistent with the biochemical data, in the context of an antiparallel, asymmetric dimer, consistent with previous structural data. Because EmrE binds TPP+ with high affinity under our conditions, and because the cysteine mutations made for the FRET experiments did not significantly change the NMR spectra, we can be confident that these experiments plausibly reproduce normal protein behaviors. However, some mutational studies indicate that EmrE functions as a parallel dimer in vivo, and further experiments are necessary to either reconcile these observations or determine where the errors originate.

Exchange between identical antiparallel, asymmetric structures allows EmrE to exchange two protons for one molecule of toxin.

In terms of the implications for fighting drug resistance in bacteria, this is an early step on a long road. EmrE is not the only drug exporter in bacteria, nor is it the most critical. It is also too soon to say whether the particular mechanism outlined here is general to the SMR family or a peculiarity of this single protein. However, these results give us confidence that the crystal structures are reliable (Katie’s group is currently is working on improving them), and that we can cleanly measure exchange rates to determine what effect drug candidates are having. The goal would be to develop accessory drugs that attack the exporters while a primary drug attacks the bacterium’s basic functions. A great deal more work is necessary before we reach that point, but this is one strategy that may allow us to defeat drug resistance, or at least prolong the usefulness of our current antibiotic arsenal.

Foldit is an online game developed by the research team of Dr. David Baker that attempts to address this problem by combining an automated structure prediction program called ROSETTA with input from human players who manually remodel structures to improve them. Even though most of the players have little or no advanced biochemical knowledge, Foldit has already had some striking results improving on computational models. An upcoming paper in Nature Structural & Molecular Biology (1) (PDF also available directly from the Baker lab) details some interesting new successes from the Foldit players.

Contrary to some reports, the Foldit players did not solve any mystery directly related to HIV, although their work may prove helpful in developing new drugs for AIDS. What the Foldit players actually did was to outperform many protein structure prediction algorithms in the CASP9 contest, and to play a key role in helping solve the structure of an unusual protease from a simian retrovirus.

M-PMV Protease

If you don’t recognize Mason-Pfizer Monkey Virus (M-PMV) as a cause of AIDS in humans, that’s because it isn’t. It causes acquired immune deficiency in macaques, however, and it has an unusual protease that may tell us useful things.

A crystal structure of an inactive mutant of HIV-1 protease in complex with its substrate. The protease monomers are in dark green and cyan, the substrate is represented as purple bonds.

Retroviruses like HIV often produce proteins in a fused form rather than as individual folded units. In order to be functional, the various proteins must be snipped out of these long polyprotein strands, so the virus includes a protease (protein-cutting enzyme) to do this. In most retroviruses, this protease is dimeric: it is composed of two protein molecules with identical sequences and similar, symmetric structures. The long-known structure of HIV protease, seen on the right (learn more about HIV protease or explore this structure at the Protein Data Bank) is an example of this architecture.

People infected with HIV often take protease inhibitors to interfere with viral replication. These drugs attack the active site, where the chemical reaction that cuts the protein strand takes place, but it has been theorized that viral proteases could also be attacked by splitting up the dimers into single proteins, or monomers. The problem is, the free monomer structures aren’t known.

This is where the M-PMV protease comes in. Although it is homologous to the dimeric proteases, M-PMV protease is a monomer in the absence of its cutting target. If we knew this protein’s structure, we could perhaps design drugs that would stabilize other proteases in their monomer form, rendering them inactive. An attempt to determine the structure using magnetic resonance data (NMR) produced models that seemed poorly folded and had bad ROSETTA energy scores. And, although the protein formed crystals, X-ray crystallography could not solve its structure either, despite a decade of effort.

The reason for this has to do with how X-ray crystallography works. If you fire a beam of X-rays at a crystal of a protein, some of the rays will be deflected by electrons within it and you will observe a pattern of diffracted dots similar to the one at left, kindly provided by my colleague Young-Jin Cho. The intensities and locations of these dots depend on the structure and arrangement of the molecules within the crystal. X-ray crystallographers can use the diffraction patterns to calculate the electron density of the protein and fit the molecular bonds into it (below, also courtesy of Young-Jin). However, the electron density cannot be calculated from the diffraction pattern unless the phases of the diffracted X-rays are also known. Unfortunately there is no way to calculate the phases from the dots.

An electron density model (wireframe) with the chemical bonds of the peptide backbone (heavy lines) fitted into it.

There are many ways to solve this problem, but not all of them work in every system. One widely-applicable approach is called “molecular replacement”. In this method, a protein with a structure similar to that of the one being studied is used to guess the phases. If this guess is close enough, the structure factors can be refined from there. In the case of M-PMV protease, however, the dimeric homologues could not be used for replacement, and an attempt to use the NMR structure to calculate the phases also failed.

Then the Foldit players went to work. Starting from the NMR structure, Foldit players made a variety of refinements. A player called spvincent made some improvements using the new alignment tool, which a player called grabhorn improved further by rearranging the amino acid side chains in the core of the molecule. A player named mimi added the final touch by rearranging a critical loop.

Going from mimi’s structure (several others also proved suitable), the crystallographers were able to solve the phase problem by molecular replacement and finally determine the protease’s structure. None of the Foldit results were exactly right, so it’s inaccurate to say that the players solved the structure. However, their models were very close to the right answer, and provided the critical data that allowed the crystal structure to be solved. Once the paper is published, you’ll be able to find that structure at the PDB under the accession code 3SQF.

We can’t know right now whether this structure will enable the design of new drugs, but the Foldit players were the key to giving us a better chance of using it for this purpose. What may be even more exciting is the possibility that Foldit could be used in other structural studies to come up with improved starting models for molecular replacement. As with any method of predicting protein structures, however, the gold standard is CASP, so the Foldit teams participated in CASP9.

2) The sequences belonging to these structures are given to computational biologists.

3) After a set period, the computational predictions are compared to the known structural results.

The Baker group generated starting structures using ROSETTA, then handed the five lowest-energy results off to the Foldit players. For proteins that had known homologues, the results were disappointing. Foldit players did well, but they overused Foldit’s ROSETTA-based minimization routine, which tended to distort conserved loops.

The nature of this problem became even more clear when the Baker group handed the Foldit players ROSETTA results for proteins that had no known homologues. In that case they noticed that players were using the minimization routine to “tunnel” to nearby, incorrect minima. You can get a feel for what that means by looking at the figure to the left.

In this energy landscape diagram, the blue line represents every possible structure of a pretend protein laid out in a line, with similar structures near each other and the higher-energy (worse) structures placed higher on the Y axis. From a relatively high-energy initial structure, Foldit players tended to use minimization to draw it ever-downward towards the nearest minimum-energy structure (red arrow). Overuse of the computer algorithm discouraged them from pulling the structure past a disfavored state that would then start to collapse towards the true, global minimum energy (green arrow).

The Foldit players still had some successes — for instance, they were able to recognize one structure ROSETTA didn’t like very much as a near-native structure. The Void Crushers team successfully optimized this structure, producing the best score for that particular target, and one of the highest scores of the CASP test. If the initial ROSETTA structures had too low of a starting energy, though, the players wouldn’t perturb them enough to get over humps in the landscape.

Thus, Baker’s group tried a new strategy. Taking the parts of one structure that they knew (from the CASP organizers) had a correct structure, they aligned the sequence with those parts and then took a hammer to the rest, pushing loops and structural elements out of alignment. This encouraged the players to be more daring in their remodeling of regions where the predictions had been poor, while preserving the good features of the structure. Again, the Void Crushers won special mention, producing the best-scoring structure of target TR624 in the whole competition.

Man over machine?

Does this prove that gamers know more about folding proteins than computers do? Some of them might, but Foldit doesn’t really use human expertise. Rather, the game uses human intelligence to identify when the ROSETTA program has gone down the wrong path and figure out how to push it over the hump. When the human intelligences aren’t daring enough, or trust the system too much, as in the case of the CASP results, Foldit doesn’t do any better than completely automated structural methods. When the human players are encouraged to challenge the computational results, however, the results can be striking. As Baker’s group are clearly aware, further development of the program needs to be oriented towards encouraging players to go further afield from the initial ROSETTA predictions. This will likely mean many more failed attempts by players, but also more significant successes like these.

Disclaimer: I am currently collaborating with David Baker’s group on a research project involving ROSETTA (but not Foldit).

Given that videogames are often demonized by research (and “research”) blaming them for everything from rudeness to the epidemic of youth violence, gamers often take a great deal of cheer from research attaching positive outcomes to videogame play. One such article that recently attracted some attention was work suggesting that playing videogames could correct amblyopia (often called “lazy eye”) in adults (1). Of course, given how negative results get oversold, it’s worth asking whether these have been, too. The paper appeared in the open-access journal PLoS Biology, so let’s open it up and take a look.

The fundamental problem that the authors are out to solve is that, while amblyopia can generally be corrected if it is treated in childhood, success tends to be rarer in adults. Knowing that video games have proven useful in improving adults’ abilities to perform a wide variety of visual tasks, these researchers decided to ask whether they could help treat amblyopia.

Figure 1 shows their experimental design. First they screened and assessed a group of adults with amblyopia. Then they divided these individuals into three groups. One group (10 individuals) played a total of 40 hours of Medal of Honor: Pacific Assault with the normal eye patched. An additional 3 individuals were assigned to a group that played SimCity Societies for an equal amount of time (it is unknown whether the author’s controlled for Societies‘ well-known liberal bias), again with the normal eye patched. The final seven individuals were given twenty hours of ordinary visual challenges (watching movies, reading, etc.) with the normal eye patched (occlusion therapy or OT), in order to ensure that patching alone wasn’t causing any observed improvements. Most individuals from the last two groups, following an intermediate assessment, then went on to play 40 hours of MOH.

As the authors note, there are several limitations to this study immediately apparent. The sample size was small, individuals were not assigned to groups randomly, and both participants and researchers knew what kind of treatment they were getting. This does not mean we should disregard the results. However, they do need to be taken with a grain of salt until the findings can be replicated in a larger sample.

And there is good cause to try to replicate these findings. Figure 2 is, unfortunately, something of a symbol party (the symbols and colors identify individual subjects by their type of amblyopia), so we’re better off focusing only on panel D, at lower right. The first item in panel D is a logMAR chart, used to measure visual acuity, and it probably looks familiar to you. Each line on the chart represents 0.1 logMAR units, and as you can see, the lower the score, the better your vision. The panel to the right of that shows the averaged data from all twenty individuals after OT and videogame therapy (VG). Here they are showing the percentage improvement in acuity in crowded conditions (the whole chart) or in isolation (a single letter). OT did not produce any improvement in acuity, while 40 hours of VG therapy produced an average 30% improvement in acuity. The other two graphs here indicate that improvement in acuity was unrelated to baseline acuity, and that the crowding index (the loss of acuity due to the presence of other letters) did not change substantially due to therapy.

This is a critical figure because, as the authors state, “reduced visual acuity is the sine qua non of amblyopia.” Substantial improvements in acuity, therefore, represent a major goal of therapy. Perceptual learning, in which participants make subtle visual judgments using their amblyopic eye, has been shown to improve acuity in adult amblyopes as well. If videogames can produce a comparable improvement, however, they may prove just as efficacious because they encourage therapy (=play) through fun.

Panels A-C of Figure 2 show the raw results and percentage improvements for each individual group. Two additional points are worth noting. Panel B shows that the 20h of occlusion therapy were ineffective, but the subsequent 40h of MOH improved acuity in all continuing individuals. However, it should be noted that while the gaming took place at the research location, the occlusion therapy was done on the individual’s own time and self-reported. This study therefore does not control for the benefits of a monitored and enforced eye-exercise regimen.

Panel C is also of interest. Although the group here is small (and the data correspondingly noisy), it appears that their acuity was improved by both SimCity and MOH. This was somewhat unexpected, because in the past positive visual effects produced by action video games have not been replicated by non-action games. Understanding why that’s not the case here may help provide some additional insights into the mechanisms by which games improve acuity in these patients. I haven’t played SimCity Societies, but having played previous SimCity iterations I know that these games often require the player to integrate a variety of visual information (traffic flow, electricity, dynamic economies) simultaneously, which may underlie the observation. Had these subjects actually played videogame chess, their improvement might have been less.

The authors went on to test the subjects’ vision in various ways. Figure 3 shows a test of positional acuity, and is rather badly made, but gets the point across that positional acuity (assessed using the funky little chart in panel A) improved in the game-playing group (panel B). This included both increases in “sampling efficiency”, related to a fitted number of correct positions extracted (out of 8) (panel C and E-SB2) and decreases in “internal noise”, or the degree to which the individual’s own eyes interfere with his assessment of position (panel D and E – SA5). The results in panel E compare improvements in efficiency and internal noise, with the three labeled graphs comparing results in the non-amblyopic eye (NAE) to the amblyopic eye (AE) before and after videogame treatment.

The authors also decided to test the effect of the games on spatial attention, as they report in Figure 4, by briefly showing the subjects a field of dots (at a size where they could be easily seen), followed by a checkerboard pattern and asking them to report the number of dots seen (panel A). Not all the individuals had an appreciable difference between the non-amblyopic eye and the amblyopic eye prior to the VG treatment (panel B). However, the degree of improvement in spatial attention tended to be greater the worse the initial condition was (panel C), including for SimCity players (symbols surrounded by dotted circles). For the worst-off subjects (dotted circle in panel B), significant improvements in accuracy and response time were observed (panel D-F).

Finally, the authors tested the stereovision of some subjects using a standard test (Figure 5). Again, substantial improvements were noted in all those tested (which excluded subjects with strabismus), to the degree that some of them were effectively cured.

These results show that playing video games produced dramatic improvements in vision for adults with amblyopia by a variety of measures. However, this study had many limitations, and nobody should go around prescribing (or self-prescribing) videogames as amblyopia therapy just yet. The sample size here was very small, and because of the way groups were assigned the various populations differed in non-trivial ways (the MOH group, for instance, was younger and more male than the others). The conditions for occlusion therapy were very different from those used in the videogame therapy, which could have contributed to the different outcomes. Even if a more comprehensive trial shows similar results, more work will be necessary to identify the best course of treatment, which I note is unlikely to take the form of a 24-hour Modern Warfare 3 binge fueled by Bawls and pizza.

That said, these results appear to justify a larger, more complete study, which we can certainly hope to see in a few years from these authors.